Observability of Boolean networks: A graph-theoretic approach

Boolean networks (BNs) are discrete-time dynamical systems with Boolean state-variables and outputs. BNs are recently attracting considerable interest as computational models for genetic and cellular networks. We consider the observability of BNs, that is, the possibility of uniquely determining the initial state given a time sequence of outputs. Our main result is that determining whether a BN is observable is NP-hard. This holds for both synchronous and asynchronous BNs. Thus, unless P=NP, there does not exist an algorithm with polynomial time complexity that solves the observability problem. We also give two simple algorithms, with exponential complexity, that solve this problem. Our results are based on combining the algebraic representation of BNs derived by D. Cheng with a graph-theoretic approach. Some of the theoretical results are applied to study the observability of a BN model of the mammalian cell cycle.

[1]  Frank Allgöwer,et al.  Observability based parameter identifiability for biochemical reaction networks , 2008, 2008 American Control Conference.

[2]  Satoru Miyano,et al.  Algorithms for identifying Boolean networks and related biological networks based on matrix multiplication and fingerprint function , 2000, RECOMB '00.

[3]  M Gámez,et al.  Observability in dynamic evolutionary models. , 2004, Bio Systems.

[4]  I. Albert,et al.  Attractor analysis of asynchronous Boolean models of signal transduction networks. , 2010, Journal of theoretical biology.

[5]  Stefan Bornholdt,et al.  Boolean network models of cellular regulation: prospects and limitations , 2008, Journal of The Royal Society Interface.

[6]  M. Aldana,et al.  From Genes to Flower Patterns and Evolution: Dynamic Models of Gene Regulatory Networks , 2006, Journal of Plant Growth Regulation.

[7]  Giovanni De Micheli,et al.  Synchronous versus asynchronous modeling of gene regulatory networks , 2008, Bioinform..

[8]  John Maloney,et al.  Scalar equations for synchronous Boolean networks with biological applications , 2004, IEEE Transactions on Neural Networks.

[9]  S. Brahmachari,et al.  Boolean network analysis of a neurotransmitter signaling pathway. , 2007, Journal of theoretical biology.

[10]  D. Elliott Bilinear Control Systems: Matrices in Action , 2009 .

[11]  Daizhan Cheng,et al.  State–Space Analysis of Boolean Networks , 2010, IEEE Transactions on Neural Networks.

[12]  Daizhan Cheng,et al.  Analysis and Control of Boolean Networks , 2011 .

[13]  M. Aldana Boolean dynamics of networks with scale-free topology , 2003 .

[14]  B. Derrida,et al.  Random networks of automata: a simple annealed approximation , 1986 .

[15]  Daizhan Cheng,et al.  Identification of Boolean control networks , 2011, Autom..

[16]  Hans A. Kestler,et al.  Multiscale Binarization of Gene Expression Data for Reconstructing Boolean Networks , 2012, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[17]  Daizhan Cheng,et al.  Input-State Approach to Boolean Networks , 2009, IEEE Transactions on Neural Networks.

[18]  Madalena Chaves,et al.  Robustness and fragility of Boolean models for genetic regulatory networks. , 2005, Journal of theoretical biology.

[19]  Darrell Williamson,et al.  Observation of bilinear systems with application to biological control , 1977, Autom..

[20]  Max Donath,et al.  American Control Conference , 1993 .

[21]  John N. Tsitsiklis,et al.  A survey of computational complexity results in systems and control , 2000, Autom..

[22]  H. Othmer,et al.  The topology of the regulatory interactions predicts the expression pattern of the segment polarity genes in Drosophila melanogaster. , 2003, Journal of theoretical biology.

[23]  Vincent D. Blondel,et al.  Observable graphs , 2007, Discret. Appl. Math..

[24]  M. Ng,et al.  Control of Boolean networks: hardness results and algorithms for tree structured networks. , 2007, Journal of theoretical biology.

[25]  Michael Margaliot,et al.  Minimum-Time Control of Boolean Networks , 2013, SIAM J. Control. Optim..

[26]  Edward R. Dougherty,et al.  From Boolean to probabilistic Boolean networks as models of genetic regulatory networks , 2002, Proc. IEEE.

[27]  Michael Margaliot,et al.  A Maximum Principle for Single-Input Boolean Control Networks , 2011, IEEE Transactions on Automatic Control.

[28]  Stuart A. Kauffman,et al.  The origins of order , 1993 .

[29]  Daizhan Cheng,et al.  Disturbance Decoupling of Boolean Control Networks , 2011, IEEE Transactions on Automatic Control.

[30]  Sui Huang,et al.  Regulation of Cellular States in Mammalian Cells from a Genomewide View , 2002, Gene Regulations and Metabolism.

[31]  R. Solé,et al.  Lyapunov exponents in random Boolean networks , 1999, adap-org/9907001.

[32]  Daizhan Cheng,et al.  Input-state incidence matrix of Boolean control networks and its applications , 2010, Syst. Control. Lett..

[33]  Aurélien Naldi,et al.  Dynamical analysis of a generic Boolean model for the control of the mammalian cell cycle , 2006, ISMB.

[34]  Ettore Fornasini,et al.  Observability, Reconstructibility and State Observers of Boolean Control Networks , 2013, IEEE Transactions on Automatic Control.

[35]  Q. Ouyang,et al.  The yeast cell-cycle network is robustly designed. , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[36]  T. Zhou,et al.  Optimal control for probabilistic Boolean networks. , 2010, IET systems biology.

[37]  Carsten Peterson,et al.  Random Boolean network models and the yeast transcriptional network , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[38]  Ashish Choudhury,et al.  Control approaches for probabilistic gene regulatory networks - What approaches have been developed for addreassinig the issue of intervention? , 2007, IEEE Signal Processing Magazine.

[39]  Michael Margaliot,et al.  Controllability of Boolean control networks via the Perron-Frobenius theory , 2012, Autom..

[40]  T Michael,et al.  Maloney, and J. , 1992 .

[41]  Edward R. Dougherty,et al.  Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networks , 2002, Bioinform..

[42]  Claudio Cobelli,et al.  Controllability, Observability and Structural Identifiability of Multi Input and Multi Output Biological Compartmental Systems , 1976, IEEE Transactions on Biomedical Engineering.

[43]  Daizhan Cheng,et al.  Realization of Boolean control networks , 2010, Autom..

[44]  R. Laubenbacher,et al.  Boolean models of bistable biological systems , 2009, 0912.2089.

[45]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .

[46]  Yin Zhao A Floyd-like algorithm for optimization of mix-valued logical control networks , 2011, Proceedings of the 30th Chinese Control Conference.

[47]  Daizhan Cheng,et al.  Controllability and observability of Boolean control networks , 2009, Autom..

[48]  Qianchuan Zhao,et al.  A remark on "Scalar equations for synchronous Boolean networks with biological Applications" by C. Farrow, J. Heidel, J. Maloney, and J. Rogers , 2005, IEEE Transactions on Neural Networks.

[49]  Randy H. Katz,et al.  Contemporary Logic Design , 2004 .

[50]  C. Espinosa-Soto,et al.  A Gene Regulatory Network Model for Cell-Fate Determination during Arabidopsis thaliana Flower Development That Is Robust and Recovers Experimental Gene Expression Profilesw⃞ , 2004, The Plant Cell Online.

[51]  Daizhan Cheng,et al.  A Linear Representation of Dynamics of Boolean Networks , 2010, IEEE Transactions on Automatic Control.

[52]  Albert,et al.  Dynamics of complex systems: scaling laws for the period of boolean networks , 2000, Physical review letters.

[53]  Tania G. Leishman,et al.  The Emergence of Social Consensus in Boolean Networks , 2007, 2007 IEEE Symposium on Artificial Life.

[54]  Z. Szallasi,et al.  Modeling the normal and neoplastic cell cycle with "realistic Boolean genetic networks": their application for understanding carcinogenesis and assessing therapeutic strategies. , 1998, Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing.

[55]  B. Drossel,et al.  Number and length of attractors in a critical Kauffman model with connectivity one. , 2004, Physical review letters.

[56]  B. Samuelsson,et al.  Superpolynomial growth in the number of attractors in Kauffman networks. , 2003, Physical review letters.

[57]  Julio R. Banga,et al.  Exponential Observers for Distributed Tubular (Bio)Reactors , 2008 .

[58]  M.H. Hassoun,et al.  Fundamentals of Artificial Neural Networks , 1996, Proceedings of the IEEE.

[59]  R. Albert,et al.  Predicting Essential Components of Signal Transduction Networks: A Dynamic Model of Guard Cell Abscisic Acid Signaling , 2006, PLoS biology.

[60]  M. Margaliot,et al.  A Pontryagin Maximum Principle for Multi – Input Boolean Control Networks ⋆ , 2011 .

[61]  S. Kauffman Metabolic stability and epigenesis in randomly constructed genetic nets. , 1969, Journal of theoretical biology.

[62]  Denis Thieffry,et al.  Logical modelling of cell cycle control in eukaryotes: a comparative study. , 2009, Molecular bioSystems.

[63]  Akutsu,et al.  A System for Identifying Genetic Networks from Gene Expression Patterns Produced by Gene Disruptions and Overexpressions. , 1998, Genome informatics. Workshop on Genome Informatics.

[64]  J. Imura,et al.  Observability analysis of Boolean networks with biological applications , 2009, 2009 ICCAS-SICE.